Data Science Lunch Series
During the academic year, our postdoctoral fellows meet most Tuesdays from 12pm – 1:30pm in the PSC 5th Floor Collaboration Space for an informal lunch with a Penn faculty guest speaker. Each speaker then leads the group in a casual discussion regarding the use of data science in their research. Discussions vary in content, but tend to focus on the applications of machine learning and artificial intelligence in various academic disciplines.
We encourage open conversation and the exchange of ideas at each lunch, thus fostering a collaborative environment among fellows and faculty alike. As such, these lunches provide an excellent opportunity for our fellows to make interdisciplinary connections and discover new ways to utilize data science in their own research.
A list of past lunch topics and faculty guests can be found below.
Meeting Topics by Semester
Fall 2024
Welcome Back Lunch
Speaker(s): None
Generating synthetic star surveys from simulated galaxy data for next-gen observatories
Speaker(s): Adrien Thob
Integrating theoretical approaches with simulations to explore learning and adaptation in physical networks
Speaker(s): Marcelo Guzmán
Creating machine learning tools for the characterization of unknown molecules in complex chemical mixtures
Speaker(s): Sourav Dey
Integrating concepts from physics, information theory, and neuroscience to investigate theoretical challenges in deep learning
Speaker(s): Pratik Chaudhari
The intersection of machine learning, distributed systems, and databases, and its application to the life sciences
Speaker(s): Zack Ives
Debugging machine learning systems to enhance system reliability
Speaker(s): Eric Wong
Integrating contemporary machine-learning principles into conventional physics-based models
Speaker(s): Nat Trask
Cosmology, gravitational lensing, and the application of machine learning to astronomy
Speaker(s): Bhuv Jain
Thanksgiving Lunch
Speaker(s): None
The effects of artificial intelligence on productivity and the labor market
Speaker(s): Daniel Rock
Spring 2024
Understanding the drivers of physical and mental well-being using social media and cell phone sensor data
Speaker(s): Lyle Ungar
The mathematical foundations of deep learning and applications to high-dimensional biomedical datasets
Speaker(s): René Vidal
The development of statistical, computational, and analytical methods to improve our understanding of crime and the functioning of the justice system
Speaker(s): Greg Ridgeway
Machine Learning for Peace: Utilizing machine learning and data analytics to enhance democracy promotion and crisis response efforts worldwide
Speaker(s): Erik Wibbels
Understanding the mechanisms that support early developing perceptual abilities in human infants
Speaker(s): Vlad Ayzenberg
Utilizing a combination of analytical theory and computation to investigate soft and living matter
Speaker(s): Andrea Liu
The influence of organized crime on fragile democracies
Speaker(s): Carlos Schmidt-Padilla
Examining the cognitive basis of human judgment and decision-making with the use of mathematical and computational models
Speaker(s): Sudeep Bhatia
The sociology of artificial intelligence
Speaker(s): Benjamin Shestakofsky
Why people commit crime and finding innovative solutions to help them stop
Speaker(s): David Kirk
Understanding complex systems by focusing on constituent pieces
Speaker(s): Kieran Murphy
Applications of machine learning to problems in algorithmic trading and quantitative finance
Speaker(s): Michael Kearns
Fall 2023
New Postdoc Welcome Lunch
Speaker(s): None
The role of data in race, ethnicity, and immigration research
Speaker(s): Dan Hopkins
Language analysis and bias in AI; Ethical recommendations for implementing machine learning-based models for detecting child abuse
Speaker(s): Desmond Upton Patton; Aviv Landau
Emergent Learning Via Sequential Error Mode Reduction
Speaker(s): Sam Dillavou
A dynamic network perspective on substance use
Speaker(s): David Lydon-Staley
Exploring the physics of life: Combining experimental and theoretical techniques across the disciplines of physics and biology
Speaker(s): Arnold Mathijssen
Studying sleep and memory using neural network modeling and empirical methods
Speaker(s): Anna Shapiro
Studying how young children learn to manage their attention and behavior in the classroom using wearable technology
Speaker(s): Andrew Koepp
Combining fieldwork, laboratory work, and computational approaches to address fundamental questions about modern human evolutionary history in Africa
Speaker(s): Sarah Tishkoff
The development and application of new deep learning methods to map out a detailed history of the Universe’s expansion
Speaker(s): Masao Sako
The role of machine learning in election race projections
Speaker(s): John Lapinski
How the separate effects of both acute stress and sleep affect both memory formation and learning pathways in the brain
Speaker(s): Brynn Sherman
How machine learning can be used to streamline analysis, optimization, and discovery tasks in the lab
Speaker(s): Andrew Zahrt
Spring 2023
Computational approaches to understanding inter- and intra-generational changes in career mobility
Speaker(s): Xi Song
Species coexistence and how we can use simple microbiology experiments and image object features to detect it
Speaker(s): Chang-Yu Chang
Using benchmarking to examine race disparities in NY state sentencing, Danish heathcare, and police satisfaction
Speaker(s): Greg Ridgeway
Identifying and classifying acoustical markers of whale songs using Google and NOAA’s Pattern Radio
Speaker(s): Eduardo Mercado (University of Buffalo)
Nationalization of voters and utilization of data science methods in political science
Speaker(s): Dan Hopkins
Trends in data science and predictive economics
Speaker(s): Hanming Fang
Tackling complexities and knowledge gaps in climate change negotiations and how data visualizations can help
Speaker(s): Michael Weisberg
Events and objects in space and time, in the mind, and in language
Speaker(s): Sarah Lee
A psychometric approach to quanitfying and classifying absolute and implicit pitch memory
Speaker(s): Karen Chow
Global ocean circulation – climate dynamics and big data in Earth Sciences
Speaker(s): Sergey Molodtsov
Transformers: The machine learning model behind ChatGPT
Speaker(s): Dimitrios Tanoglidis
Fall 2022
Challenges in applying deep learning to cosmological surveys
Speaker(s): Bhuv Jain
Classifying stars and galaxies
Speaker(s): Dimitrios Tanoglidis
Understanding the field of data science
Speaker(s): Vijay Balasubramanian
Methods of inquiry in the social sciences
Speaker(s): Roland Neil
Issues in interpreting and establishing significance in research findings
Speaker(s): Eleni Katifori
Evaluating strength of establishing causality and scientific rigor in research
Speaker(s): Konrad Körding
Computational modeling of visual processing; How to locate data and applications of machine learning to do so
Speaker(s): David Brainard; Kieran Murphy
Model for predicting police officer features and rounds discharged in gun shootings; Friction: a short history and discussion about sparse, messy data
Speaker(s): Greg Ridgeway; Sam Dillavou
Efficacy of bed bug infestation disclosure; Dementia prediction using speech features
Speaker(s): Sherrie Xie; Sunghye Cho
Understanding the replication crisis in research, evaluating common sense; Multi-layer network analysis in relating brain structure to exposome in children
Speaker(s): Mark Whiting; Ivan Simpson-Kent; Andrew Connolly (University of Washington)
Evolution of baby names and popularity of dog breeds; Computational principles and communicative needs that shape color naming systems in natural language
Speaker(s): Josh Plotkin; Colin Twomey